Saved in:
| Hovedforfatter: | |
|---|---|
| Format: | Recurso digital |
| Sprog: | |
| Udgivet: |
Zenodo
2026
|
| Fag: | |
| Online adgang: | https://doi.org/10.5281/zenodo.20045638 |
| Tags: |
Tilføj Tag
Ingen Tags, Vær først til at tagge denne postø!
|
Indholdsfortegnelse:
- We present a knowledge distillation framework that compresses a 137,763-parameter recurrent ensemble (LSTM + GRU) into a 3,328-parameter Liquid Continuous-time Closed-form (LiquidCfC) network, achieving 41.4x parameter reduction for directional classification on forex time series. The teacher achieves a pooled bootstrap Sharpe ratio of 71.4 (95% CI: [58.4, 85.1]) while the student achieves 16.3 (95% CI: [14.76, 17.89]), demonstrating statistically significant predictive fidelity under extreme compression. We introduce a deployment gate criterion (G3) based on KL divergence at unit temperature, providing a principled accept/reject mechanism for compressed model deployment. Validated via Monte Carlo block bootstrap (10,000 resamples) on expanding-window walk-forward cross-validation across four major currency pairs.